Adaptive Marketing Using Insight Driven Customer Interaction

ABSTRACT

A system and method for adaptive marketing using insight driven customer interaction. The invention uses a closed-loop process for developing insight that may be used to refine further customer interactions. Results of a first customer interaction such as a marketing campaign are stored in a database. The results may be used to retrain predictive models and gain new insights regarding how customers are responding to marketing campaigns. The insights may be used to refine the offers delivered to customers or to extend additional offers in an effort to increase the likelihood that customers will redeem the offers. After each marketing campaign, the results are stored in the database. New and/or modified offers are created based on insights provided by the results of past campaigns. This process may be repeated such that subsequent campaigns are based on insights generated by the predictive models. The insight enables businesses to better target customers with better offers. These offers can be delivered through ensuing marketing campaigns or, through any form of interaction that the business has with the targeted customers.

RELATED APPLICATIONS

This application is a continuation of application Ser. No. 10/302,395,filed Nov. 22, 2002, related to commonly assigned U.S. Pat. No.7,047,251, issued May 16, 2006, and titled “Standardized CustomerApplication And Record For Inputting Customer Data Into AnalyticModels”, and co-pending U.S. patent application Ser. No. 10/302,418,titled “Multi-Dimensional Segmentation For Use In A CustomerInteraction”, both filed Nov. 22, 2002, all of which are incorporatedherein by reference. This application is also related to commonlyassigned co-pending U.S. patent application Ser. No. 10/014,840, filedOct. 22, 2001, and titled “Real-Time Collaboration and WorkflowManagement for a Marketing Campaign,” which is incorporated herein byreference.

FIELD OF THE INVENTION

The invention relates to adaptive marketing, and more particularly toadaptive marketing using insight driven customer interaction.

BACKGROUND OF THE INVENTION

Many businesses use a campaign process to deliver marketing offers to avariety of consumers. The campaign process may be, for example, bytelephone or by mass mailing. In order to define the campaigns toexecute, the business may gather and aggregate information about theircustomers from a variety of data sources, both from within their companyas well as from third party data providers. After gathering the consumerinformation, the businesses may decide to separate customers intogroupings, customer segments, which have similar characteristics. Thebusinesses may then create a specific list of consumers that thebusinesses hope will respond positively to the campaign. Sometimes,these lists may be produced using generalized marketing responsemodels—models developed on generalities about the firm's customersrather than specifics about likely customer response to forthcomingcampaign offers. These general models are sub-optimal. But more often,the lists are purchased from third-party vendors, or extracted frominternal databases using SQL-based rules. Not infrequently,telemarketing relies simply on lists of bare telephone numbers selectedfrom particular area codes and exchanges, with no information about theprospect until the contact is actually established.

This process typically can be time consuming and deliver sub-optimalresults. Businesses typically employ personnel to search for theconsumer information. The personnel may individually search a number ofdisparate databases attempting to gather the consumer information. Thiscould include information that helps to identify the customer (e.g.,name, address, phone, electronic mail address, etc.), information onproducts or services the customer has purchased in the past, and anyadditional contextual information captured during past contacts with thecustomer. Oftentimes, this information is stored in disparate databasesin inconsistent formats, making it very difficult to formulate a total,integrated view of a customer. The databases may also contain stale datathat produces poor or even erroneous results.

Businesses may attempt to purchase additional information about existingor prospective customers from third party data providers (e.g., Equifax,etc.). Types of information purchased may include demographic data(e.g., income level, house size), lifestyle data (e.g., activities thecustomer participates in, etc.), and interests (e.g., informationindicating the customer enjoys eating at restaurants, going to seemovies, etc.). Oftentimes, businesses find it challenging to integrateexternally purchased data with their own customer data. When data ismerged from multiple data sources, sophisticated programming skills arerequired to link records as well as to aggregate information andcalculate values that could be useful to predict customer behavior.Further, the extraction of data from multiple sources to driveanalytical modeling can be a very laborious, time consuming processgiven the number of joins that have to be written. Oftentimes,businesses do not have common extract procedures meaning that—newextract routines have to be written each time a new form of dataanalysis needs to be performed.

More advanced database marketers make heavy use of analytics andmodeling. Customer segmentations based on commercially availabledemographics, lifestyle, and lifestage data are often used to helpdefine campaigns. These data are also used to target individuals.Unfortunately, because these data are usually compiled at the zip codeor census-tract level, application to individuals for targeting issubject to a great deal of error. Propensity models (models comparingattributes of prospect lists to attributes of existing customers) areoften developed by businesses and used to develop targeting lists ofpersons who look like existing customers, hence may have a greaterpropensity to respond to the business' marketing campaigns. Some moresophisticated businesses are able to develop response models (modelsbased on respondents to actual campaigns); these models tend tooutperform the other list generating methods. However, these moresophisticated models require more sophisticated methods and better data.The cost of developing these models can be high.

For example, a typical model development process may require two orthree people and four to twelve weeks (i.e., 12-36 people-weeks) toextract the required customer data and build an analytic model. Thendeveloping a scoring algorithm may take a person four additional weeks.Thus, targeting models are costly. The cost and time required for modeldevelopment encourages the development of generalized marketing modelsthat are often used for a year or more. Generalized models are commonlyoutperformed by as much as one hundred percent (100%) by modelsdeveloped specifically for a particular campaign or offer. Over time,models degrade in performance, but are often used long after theirperformance peak. This results in diminished marketing returns and oftenresults in abandonment of the use of models for targeting. A secondproblem is that the data used to create the predictive models andultimately define and execute the marketing campaigns is old by the timethe models are run, leading to out of date model results and poor offeracceptance rates for the resulting marketing campaigns.

The time-consuming conventional modeling and marketing processes cannotsupport rapid test and learn iterations that could ultimately improveoffer acceptance rates. After completing a marketing campaign, thepersonnel may gather the results of the campaign to determine a successrate for the campaign. The results, however, are typically noteffectively fed back into the customer information database and used tore-analyze predictive customer behavior. Without an effectiveclosed-loop, businesses lose the ability to retrain their analyticalmodels and improve their campaigns by defining campaigns that have agreater return.

The effect of the previously described issues extend beyond marketingcampaigns to all forms of interaction. A business' inability to executean effective, closed loop process to tailor their marketing campaignsaffects all forms of customer interaction. Ideally, a business shouldstrive to deliver the right message to the right customer through thebest channel. Customers who are the target of an outbound marketingcampaign should be able to receive the same offer should they interactwith the business through any interaction channel (e.g., web, phone,retail branch, etc.) to perform a service transaction, salestransaction, etc. However, since traditional methods prevent thebusiness from quickly generating reliable, targeted offers for customersbased upon predictive analytical models and refined through rapid testand learn iterations, they are unable to deliver optimized marketingoffers tailored to their customers and prospects across all forms ofcustomer interaction; best offer to the right customer through the bestchannel.

SUMMARY OF THE INVENTION

The invention relates to a system and method for adaptive marketingusing insight driven customer interaction. The invention uses aclosed-loop process for developing insight that may be used to refinefurther customer interactions. Results of a first customer interactionsuch as a marketing campaign are stored in a database. The results maybe used to retrain predictive models and gain new insights regarding howcustomers are responding to offers delivered in marketing campaigns. Theinsights may be used to refine the offers delivered to customers or toextend additional offers in an effort to increase the likelihood thatcustomers will redeem the offers. After each marketing campaign, theresults are stored in the database. New and/or modified offers arecreated based on insights provided by the results of past campaigns.Adaptive response models are developed to further refine the targetinglist to those persons more likely to respond to the new offers. Thisprocess may be repeated such that subsequent campaigns are based oninsights generated by the predictive models. The insight enablesbusinesses to better target customers with better offers. These offerscan be delivered through ensuing marketing campaigns or, through anyform of interaction that the business has with the targeted customers.

In one embodiment, a database is created that stores customer data for aplurality of customers. A Customer Analytic Record (“CAR”) applicationis developed as a database object to be used to extract, transform, andformat the customer data needed for all subsequent customer segmentationand predictive modeling. The CAR allows extraction of customer datausing predetermined transformations, including custom transformations ofthe underlying behavioral data. These transforms create new variablesderived dynamically by the CAR application. These derived variables areuseful in generating new insights into customer behavior. The CARprovides a database object that can be queried like any database table.It is a set of database “views” defined in native SQL that provides astandard format for all data retrieved from the database for analyticmodeling and analysis. The CAR is a dynamic view of the customer recordthat changes whenever any update is made to the database. The CARenforces standardized, best-practice data transformations of customerdata, and reduces errors and omissions that typically plague modelingactivities. The definition of the CAR provides complete documentation ofeach data element available for use in models and analytics, includingthe transformations. The customer information is formatted into astandard CAR for each customer (referred to as a Prospect AnalyticRecord (“PAR”) when the customers involved are not current customers ofthe company). Although CAR/PAR refer to different record types, forsimplicity, the discussion is directed to CAR even though the inventionis also applicable to PAR. Essentially the CAR is a virtual “flat”record of data available for customer analytics, while the PAR is avirtual “flat” record of data available for prospects (usuallycommercial marketing data). The CAR record usually contains the PARrecord plus records formed from customer data.

The information provided by the CAR may be used to segment customersaccording to a plurality of characteristics. The customers may becategorized into a segment of customers having similar characteristics.The segments may be used to classify customers according to a likelihoodof the customers accepting a particular marketing offer. Amulti-dimensional segmentation approach may be applied to cross-segmenta plurality of customers so that the crossed segments can be profiledfor more precise targeting. The segment of customers having the highestratings may be selected for whom a proposed marketing offer may bemodeled. A predictive model may be generated to predict the behavior ofa targeted segment. The analysis may assign propensity scores to eachcustomer in targeted segments so that specific customers can be selectedto receive offers delivered through marketing campaigns.

As the marketing campaign is executed, the results of the marketingcampaign may be captured. The results may indicate the success of themarketing campaign and may include additional metrics useful inunderstanding the dynamics of the campaign. The results may be used torefine further marketing campaigns. The database storing the customerdata may be updated with results corresponding to each customerresponse. This provides further insight regarding what types of offers acustomer responds to, which customers are more likely to respond, etc.This feedback may then be used to execute additional predictive analyticmodels for refining existing marketing campaigns or generating newmarketing campaigns.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram of a method for adaptive marketing usinginsight driven customer interaction according to one embodiment of theinvention.

FIG. 1B is a table illustrating segmented customer data according to oneembodiment of the invention.

FIG. 1C is a table illustrating the definition of a marketing campaignaccording to one embodiment of the invention.

FIG. 2 is a block diagram of a system for adaptive marketing usinginsight driven customer interaction according to one embodiment of theinvention.

FIG. 3 is a block diagram of a method for segmentation and predictivemodeling for adaptive marketing using insight driven customerinteraction according to one embodiment of the invention.

FIG. 4 is a block diagram of a system for segmentation and predictivemodeling for adaptive marketing using insight driven customerinteraction according to one embodiment of the invention.

FIG. 5 is a block diagram of a method for creating a standardized inputfor analytic models for adaptive marketing using insight driven customerinteraction according to one embodiment of the invention.

FIG. 6 is a block diagram of a system for creating a standardized inputfor analytic models for adaptive marketing using insight driven customerinteraction according to one embodiment of the invention.

FIG. 7A is a method for developing multi-dimensional segmentationaccording to one embodiment of the invention.

FIG. 7B is an illustration of relationships among characteristics thatmay drive customer behavior.

FIG. 8 is a system for developing multi-dimensional segmentationaccording to one embodiment of the invention.

FIG. 9 is a method for adaptive marketing using insight driven customerinteraction according to one embodiment of the invention.

FIG. 10 is a system for adaptive marketing using insight driven customerinteraction according to one embodiment of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The invention relates to adaptive marketing using insight drivencustomer interaction. The invention can be extended to other customerinteractions in which insight improves the interaction between anenterprise and its customers. FIG. 1A illustrates a method for adaptivemarketing using insight driven customer interaction according to oneembodiment of the invention. Initially, a database may be created tostore customer data, step 10. The customer data may be collected throughinternal, external, and/or business partner data sources. The databaseused for storing customer data may be any known data storage mechanism,generally a relational database often referred to as a data warehouse.According to one embodiment of the invention, the data warehouseplatform used for storing the customer data is powered by NCR's Teradatasystem.

The data may be extracted from the database, step 12. In one embodiment,a CAR/PAR application may be used to extract data from the database andthen transform, aggregate, and combine the data into standardizedvirtual flat file records for each customer, such as a customer analyticrecord (CAR) for existing customers and a Prospect Analytic Record (PAR)when the targeted consumers are not current customers of the company.The step of transforming the data may include custom transformations tofill the calculated CAR fields. The CAR may be used as input todescriptive and predictive models to determine how consumers are likelyto respond to marketing offers. The models may also be used to predict alikelihood of attrition or other behaviors.

According to one embodiment of the invention, the CAR may be producedvia a view. A database view is a virtual query. The CAR is usuallywritten as a set of views that do all the “flattening out” of the dataand also computes the ratios, etc. that may be used in modeling.Consider the following SQL statement:

select cust_id, acct_balance_RTM, max( account_balance) from txn_tablewhere behavior_segment = 1 and acct_balance_RTM < 1 order by cust_id;

This statement may be executed by a database system to return a sortedlist of customer ids, account balance ratio-to-mean and their maximumaccount balances. The result may be a table if left in the database or aflat file if exported. The query may be changed to generate a viewusing, for example, the following:

create view CAR_rtmmax_balance as select_cust id,    acct_balance_RTM,   max( account_balance) from txn_table

This creates a virtual table or “view” in the database. The CAR prefixindicates that the view CAR_rtmmax_balance is a component of the overallCAR application. A user may now query CAR_rtmmax_balance as though thiscomponent was a table in the database:

select * from CAR_rtmmax_balance where behavior_segment = 1 andacct_balance_RTM < 1

The view looks just like a table to the user. Because CAR_rtmmax balanceis a view, a query automatically returns the latest values that havebeen loaded into the database. If the information in the view was storedin a permanent database table (of the same or different name), a specialupdate process would be necessary in order to capture changes made to abase table, txn_table. The optimal configuration for the CAR developmentis to define is a set of dynamic views of the customer data within thedata warehouse. This enhances data integrity in the resulting analyticdata set.

The CAR/PAR data may include identification and behavior fields. Theidentification fields may be for household information such as ahousehold identifier, address, and phone number and household individualinformation such as name and electronic mail address. The behaviorfields summarize transaction information and contain statisticaltransformation of this data for analytical use. Examples include accountsummary data, ratio to mean and z-score calculations, moving average andmoving difference calculations over a specified period of time, logtransformations and slope calculations.

The CAR may also include demographic fields. The demographic fields mayinclude, for example, income level and house size. The demographicfields also include fields pertaining to lifestyle and interest. Thelifestyle fields may include, for example, whether the individual is adomestic, enjoys the outdoors such as hiking, biking, camping, walking,running, etc., and whether the individual is athletic or enjoys sports.The interest fields may indicate, for example, whether the individuallikes to travel, play video games, drink wine, play sports, watchsports, read, etc. Preferably, each of the fields and data included inthe CAR may be cross-referenced to an individual's household. This maybe performed by linking a household identifier to an individual'sidentifier.

The CAR may also include a contact history. The contact history mayinclude information related to promotions offered to a customer,promotions redeemed by the customer, elapsed time for the offer to beredeemed, and telephone calls made or emails sent to the customer by acontact center or received from the customer by a contact center. Thecontact center may be, for example, an on-line support system, a salesrepresentative center, etc.

The CAR also preferably includes model scores. The model score fieldsmay include an individual identifier such as a primary key, a modelprimary segment or decile such as segment number or predictive modelscore decile, and a model score such as a predictive model score or aresponse model score. The model score may also include a householdidentifier that may be used to cross-reference a customer to ahousehold.

In addition to identification fields, behavior fields, demographicfields, contact history fields, and model score fields, the CAR alsopreferably includes fields representing product ownership information.Product ownership includes a complete listing of all of the products andservices that a customer has previously purchased from the business.Such product ownership information in the CAR record provides a morecomplete picture of each customer and may be used in determining whichoffers to extend to certain customer segments.

Segmentation may be performed based on the data extracted, step 14. Thesegmentation process may establish customer segments, for example, 6-9groups of customers that are used to drive the campaign strategy anddesign. The customer segments may be created based on similarcharacteristics among a plurality of customers. Segmentation is usuallybased on a random sample set of customer records extracted through theCAR views. For example, the data extracted from the database may be forapproximately thirty (30) million customers. Segmentation, however, maybe performed only on a percentage, for example, ten (10) percent, of thecustomer records. Therefore, segmentation may be performed for three (3)million customer records instead of thirty (30) million. Preferably, thethree (3) million customer records are randomly selected, however, anymanner of selecting the customer records may be used. Although a fewernumber of customer records may be used for segmentation, by using arandom sample set of a percentage of customer records, a fairly accuratedepiction of the customers may still be obtained. Some technologiesenable segmentation of the complete customer record. This may beadvantageous for segmentation, however, a good random sample willusually be much easier to handle and can produce statistically validresults.

Once the customer segments have been defined, they are profiled in termsof behavior, value, and possibly demographic, lifestyle and life-stagedata. This allows the business users to understand and “name” thesegments. If the business user cannot name the segments, then theprocess is repeated until he can. This ensures that the segments arestatistically valid and have meaningful business value. At this time,all of the customer records in the database are updated to reflect thesegmentation results. FIG. 1B demonstrates a sample segmentationprofile. Segments have been defined as customers having mortgages only,big savers, small savers, normal savers, new customers, and entrenchedcustomers. Each segment is described through a description, percentageof sample population falling within the segment, a lift value, assetaccounts, loan accounts, tenure, transaction activity, demographics,etc.

After segmenting the customer records, a marketing campaign may bedefined for one or more customer segments based upon what's known aboutthe customers in the segment, step 16. For example, referring to FIG.1B, the lift value may indicate a likelihood for a customer segment toredeem an offer. The lift value may be calculated by dividing a numberof accounts held by a predetermined number of customers divided by thenumber of customers. The lift value provides a factor that may be usedto target specific segments and reduce the total number of customers towhom an offer is to be communicated. A reduction in costs is achievedbecause fewer telephone calls or mailings are necessary to achievesubstantially the same or higher response. For example, if a marketingcampaign results in 100 new accounts for a bank out of the 1,000,000customers contacted with the marketing offer, 0.0001 is the calculatedlift. By using adaptive marketing through the present invention, 100 newaccounts may be opened by contacting just the 400,000 customers mostlikely to respond (as determined by the model). In FIG. 1B, becausesegments 2, and 6 have a high lift value, these segments may be targetedfor a marketing campaign. By using adaptive marketing, a 0.0025 liftresults, which means that for the same number of sales, 600,000 fewercustomers needed to be contacted. Because, each contact has a cost, areduction of 600,000 contacts in an outbound telemarketing context maysave millions of dollars in marketing costs.

Next, a predictive model may be created/trained to determine thespecific offers to provide to customers based on the data extracted,step 18. Predictive models may be created when a first marketingcampaign is defined. Predictive models are often developed usingstatistical methods like logistic regression, but data miningtechnologies like neural nets, decision trees may also be used. FIG. 1Cis a table that provides a description, hypotheses, and potential offersfor segments 2 and 6. Prescriptive models may be defined and executed todetermine which of these offers to provide and which specific customersin each segment should be targeted. After the first campaign isexecuted, the predictive model may be trained using insight obtainedfrom the first marketing campaign. Such training of analytic models iswell known in the art, as are the tools to accomplish the modeling. Forexample, software developed and sold by KXEN, Inc. (Knowledge ExtractionEngines) of San Francisco, Calif. may be used.

After training a predictive analytic model, a marketing campaign for oneor more customer segments may be executed, step 20. The marketingcampaign may be run by communicating offers to the customers through acustomer interaction. The customer interaction may be, for example, atelephone call to the home of a customer or a mailing of an offer to thecustomer's home.

As the campaign is executed, the results of the campaign may becaptured, step 22. It should be noted that the invention reducesmarketing campaign cycle times and provides adjustments for competitivepositioning in a changing market. The invention achieves this by notrequiring an arduous data extraction, transformation, modeling, andscoring process to have to be repeated each time a marketing campaign isdesired as discussed above. Marketing cycle times are reduced bysimplifying the extraction and transformation of all the customer dataelements needed for analytic modeling. The CAR application includes allthe necessary logic to extract the data from a database and all of thetransformations needed to create additional customer data elements forsegmentation and predictive modeling. Pre-built data models can also bere-used or leveraged since the models all rely on the same standard datainputs. The invention uses insight developed from prior campaigns toupdate a customer database.

The results may include, for example, the number of offers redeemed,which customers redeemed the offer, the time elapsed between the offerpresentation and redeeming of the offer, and other information. Thedatabase may then be updated automatically with the results of themarketing campaign, step 24. This update may be done via a series of SQLupdate statements, for example. The marketing campaign results provideinsight regarding a customer's behavior toward redeeming offers. Theinsight may be, for example, what types of offers a customer is likelyto accept, which customers are more inclined to accept an offer, howquickly a customer redeems an offer, etc. This information may be usedto refine further customer interactions to increase the number of offersaccepted. Thus, through the interaction with the customer, insight(knowledge) is gained that is used to improve future interactions, suchas marketing campaigns. This may be performed by repeating the steps ofthe method for adaptive marketing using insight driven customerinteraction. Based on new customer data extracted (including part or allof the updated data that is the insight gained from the priorinteraction), the predictive model may be trained resulting in a moreaccurate picture of anticipated customer responses to marketing offers.The adaptive model is usually developed to support each new campaign.Due to the rapidity of model development, enabled by this process,models can be developed to support each new campaign, then re-trained(adapted) to provide a mid-campaign correction if necessary. Thisprocess may be repeated for any desired number of customer interactions.

FIG. 2 illustrates a system 50 for adaptive marketing using insightdriven customer interaction according to one embodiment of theinvention. The system 50 may include a database creating module 52 thatprovides a database for storing customer data. A customer dataextracting module 54 may be used to extract, transform, and format thecustomer data from the database for segmentation and training apredictive model. A customer segmenting module 56 may be used to segmentcustomer records that provide a profile of a customer into segments ofcustomers that have similar characteristics. A campaign defining module58 may be used to define a campaign for one or more customer segmentsidentified by customer segmenting module 56. A predictive modelcreating/training module 60 may be used to create/train a predictivemodel for determining how a customer may react to a marketing offer. Thepredictive model may be created when a first marketing campaign isdefined and trained when subsequent marketing campaigns are definedusing insight obtained from the first marketing campaign. The predictivemodel may be used to predict customer behavior regarding one or moreoffers communicated to the customer. The predictive model may indicate,for example; that the customer is highly likely, likely, unlikely, orvery unlikely to accept the offer. This assists in defining targetedtreatments, offers, and marketing campaigns based upon an integratedview of the customer resulting in improved marketing campaigns.

Based on the predictive model, a campaign executing module 62 may beused to define and execute one or more campaigns to be communicated tothe customer. The campaigns may include, for example, telephoning ormailing offers to the customers that are likely to accept the offer.

As a campaign is executed, the results of the campaign may be gathered.The results may include, for example, the number of offers accepted, theidentification information for the customers who accepted the offers,the length of time between initiating the customer interaction andacceptance of the offer, and other information. The campaign results maybe gathered using campaign results gathering module 64. The databasestoring the customer data may then be updated with the campaign resultsusing database updating module 66. The campaign results may be used torefine additional customer interactions with the customers. For example,the customer results may show that a particular offer was not wellreceived by the customers or that a particular segment of customersredeemed a large percentage of a particular offer. Therefore, based onthis information, additional customer interactions may be refined totarget the customer segment that accepted a large percentage of theoffers. The offer not well received by the customers may be altered toinduce future acceptance of the same or a similar offer or possiblycanceled. The offers may be, for example, coupons to be used atrestaurants, movie theaters, amusement parks, etc. or for servicesrendered.

FIG. 3 illustrates a method for training a predictive analytic modelaccording to one embodiment of the invention. A predictive analyticmodel may be created/trained by choosing a customer data sample set,step 100. The sample set may be, for example, a percentage of customerrecords from a total number of customer records such as ten (10)percent. The sample set may be segmented to divide customers intosegments having similar characteristics, step 102. The customersassociated with each segment may be given a score to identify to whichcustomer segment each customer belongs, step 104. A marketing campaignfor one or more of the customer segments may be defined, step 106. Themarketing campaign may include one or more offers to be delivered to thecustomers. A predictive marketing model is constructed to assess eachcustomer's propensity to respond to an offer in the targeted customersegments, step 108.

The results from running the marketing model are used to scoreprospective customers, step 110. The prospect set may be used fordetermining which offers are to be communicated to which customers.According to one embodiment of the invention, KXEN technology may beused to build the marketing model and score the prospect set. Accordingto one embodiment, the customers receiving the highest score may bedeemed most likely to accept a particular offer. A lower score mayindicate a lower acceptance response to the offer. Therefore, offers arepreferably communicated to customers receiving high scores.

FIG. 4 illustrates a system 150 for training a predictive analytic modelaccording to one embodiment of the invention. The system 150 may includea customer data sample set choosing module 152. The customer data sampleset choosing module 152 may choose a sample set of customer data to beused to group customers into segments. A customer segmenting module 154may be used to segment customer records according to similarcharacteristics. A customer segment scoring module 156 may be used toupdate customer records to reflect their assigned segment created bycustomer segmenting module 154. Based on the profiles of each of thedefined customer segments, a marketing campaign may be defined usingcampaign defining module 158. The marketing campaign may propose whichoffers are to be targeted to which customer segments.

Next, a marketing model is built to assess customers' propensity torespond to proposed offers using the marketing model building module160. The marketing model may then be used to score a prospect set ofcustomers using prospect set scoring module 162. Based on the resultsgenerated by the prospect set scoring module 162, the customers thatreceive a high score may be communicated an offer.

FIG. 5 illustrates a method for creating a standardized input foranalytical models according to one embodiment of the invention. Adatabase for storing customer data may be initially created, step 200. ACAR may be created to extract, transform, and format the customer datato be used as input for an analytic model, step 202. The CAR provides adatabase object that may include one or more database views and dynamicand temporary tables. Dynamic tables are automatically developed at thebeginning of a query and destroyed when completed whereas temporarytables are usually pre-loaded with data and persist after the query hasfinished. Dynamic and temporary tables are typically used forperformance reasons or to store data in a certain manner.

The customer data may be extracted from the database by running one ormore queries on the CAR, step 204. The SQL queries against the CAR maythemselves create additional variables by operating on the data returnedby the CAR queries, step 206. Some examples of these transformations areslopes and ratio calculations. Slopes may be calculated when a customerrecord contains time series transaction data (e.g., number oftransactions per week, account balance per month, etc). Plotting thesetrends on a time graph allows a straight line to be fitted through thepoints. The slope of the line is an indicator of whether the rate oftransactions is increasing or decreasing over time. Ratios provideanother good way to analyze data (e.g., the ratio of one customer'saccount balance compared to the average balance of all customers in thesegment). These variables provide additional predictive power to models.For example, ratios-to-means and slopes are very important inputs toretention models. When the slope of a customer's account balance isdecreasing at a high rate, determined by the ratio of decline comparedto the mean, it's a good predictor that the customer is planning to takehis or her business to another establishment. If the business recognizesthis trend in advance and identifies that this is a high value customer,it can take measures to attempt to retain the customer.

The CAR is a method of setting up virtual stored queries that includetable fields as well as calculated fields created using capabilities ofa Data Base Management System (DBMS) and a structured query language(SQL) such that the stored queries present to the user a virtual flatfile that may be used as input to an analytic engine. The capabilitiesof the DBMS and SQL may include, for example, (1) functional objectsavailable within SQL to perform certain statistical and mathematicaloperations on data retrieved from the database: e.g., average, standarddeviation, ranking, moving averages, regression, logarithmictransformation, sequence analysis, etc.; (2) ability to process andcomplete queries that may contain one or more mathematical orstatistical operations against a 3rd Normal Form database, which impliesthe ability to join many tables to produce the query answer sets; (3)the capability to express these complex queries as an object in thedatabase, either as a view or as a pre-defined function usable in a SQLquery; (4) the capability to nest these views and otherwise combine theminto other higher-level views or in ad hoc SQL queries; (5) the abilityto create dynamic or temporary relational tables on the fly (during thecourse of execution of queries); (6) the ability to define and querythese views and other data objects within a very large relationaldatabase that may be dozens of terabytes in size, that may containtables with billions of rows of data.

If additional variables are created, from CAR data or database dataduring modeling or analysis processes, the CAR may be modified toinclude the additional variables, step 208. In this manner, theadditional variables become part of the CAR and are available for futuremodeling and analytic requirements.

FIG. 6 illustrates a system 250 that may be used for creating astandardized input for analytical models according to one embodiment ofthe invention. The system 250 may include a database creating module252. The database creating module 252 may provide a database for storingcustomer data. A CAR may be created for the purpose of extracting,transforming, and formatting the customer data to be used as input foran analytic model using CAR creating module 254. The CAR preferablyprovides a database object that may include one or more views. Acustomer data extracting module 256 may be used to extract the customerdata from the database using the CAR. The customer data may be extractedby running one or more queries against the view(s). Based on the dataqueried, additional variables may be created by the CAR view(s) usingadditional variable creating module 258. If additional variables arecreated, the CAR may be modified to include the additional variablesusing CAR modifying module 260. The CAR may then be used to providestandardized input for analytical models. The CAR preferably includesall of the information necessary to predict customer behavior and definetargeted customer interactions with a customer.

FIG. 7 A illustrates a method for segmenting data representing aplurality of customers for use in a customer interaction according toone embodiment of the invention. Customer data may be segmentedaccording to a first characteristic, step 300. The first characteristicmay include, for example, behavior data. To execute behaviorsegmentation, preferably, only the variables of the CAR, both direct andderived, that reflect a customer's behavior are used. Examples of suchvariables that reflect behavior are the number of transactions, rate ofincrease of the number of transactions, average value per transaction,etc. Preferably, demographic variables are not used for generatingbehavioral segmentation. After the segments have been identified, thesegments may be profiled with all of the variables including anydemographic variables.

The customer data may then be segmented according to a secondcharacteristic, step 302. The second characteristic may be, for example,value data. For the value segmentation, the variables that are used arepreferably indicative of customer value. In one embodiment, the customerlifetime value may be used as the driving variable for thissegmentation. Other value indicators like profitability, etc. may beused. A determination may then be made regarding whether the customerdata is to be segmented according to a third characteristic, step 304.If the customer data is not to be segmented according to the thirdcharacteristic, a two-dimensional matrix for cross-segmenting thecustomers by both behavior data and value data may be generated, step306. To do this, the value segments and the behavior segments may be“overlayed” and the behavior-value crossed segments may be profiled toget a joint view. If, however, a determination is made that the customerdata is to be segmented according to a third characteristic, thecustomer data may segmented by a third characteristic, step 308.

After segmenting the customer data according to a third characteristic,a three-dimensional matrix for cross-segmenting a plurality of customersby the first, second, and third characteristics may be generated, step310. The segmentation may be expanded to many data types generating amulti-dimensional hypercube that more completely characterizes thecustomers.

In the behavior-value segmentation embodiment of the invention, thesegmentation may be performed by using a cluster analysis algorithm toidentify latent clusters in the data. Most algorithms typically identifyclusters that have a low ratio of within cluster variability to acrosscluster variability using some standard distance metrics. According toone embodiment of the invention, the algorithm used is driven by abusiness objective. This in turn permits the distance metrics that areused in the cluster analysis to be calibrated in the context of thestated business objective. In other words, the invention generatesclusters that are more closely aligned with the business case and istherefore a semi-supervised segmentation as opposed to a completelyunsupervised segmentation.

The approach to two dimensional modeling described above regardingbehavior and value data may be applied to other characteristics that mayinfluence customer behavior, such as, for example, attitude,satisfaction, brand experience, brand attachment, brand utility, andcategory involvement. Attitude may reflect a holistic view of a firmheld by a customer. Satisfaction may be a day-to-day satisfactionresulting from current transactions between the customer and a firm. Thebrand experience may be the cumulative effect of day-to-daysatisfaction. Brand attachment may be an attitude or feeling toward abrand by a customer. For example, brand attachment may be stronglyinfluenced by advertising. Non-customers such as HARLEY DAVIDSON™ brandmotorcycle aficionados may have a strong brand attachment. Attachmentmay be reinforced positively or negatively by brand experience. Brandutility may be a need for goods or services provided by a firm. Thebrand utility may be affected by lifestyle and life-stage factors.Category involvement may be a need for specific products and/orservices. Category involvement may also be affected by lifestyle andlife-stage factors.

Applicants of this invention have found that there may be relationshipsbetween these characteristics that may ultimately affect customerbehavior. FIG. 7B illustrates that: attitude drives behavior; behaviordrives value; the relationship experience felt by the customer impactscustomer satisfaction; satisfaction affects brand experience; brandexperience affects brand attachment which is also impacted byadvertising; life-stage and lifestyle affect brand utility andinvolvement by category; attitude is affected by brand, experience,brand attachment, brand utility, and involvement by category; etc.

FIG. 8 illustrates a system 350 for segmenting data representing aplurality of customers for use in a customer interaction according toone embodiment of the invention. The system 350 may include a firstcharacteristic segmenting module 352. The first characteristicsegmenting module 352 may segment the customer data according to a firstcharacteristic. The first characteristic may be, for example, behavior,attitude, value, satisfaction, brand experience, brand attachment, brandutility, or category involvement. A second characteristic segmentingmodule 354 may be used to segment the customer data according to asecond characteristic. The second characteristic may be, for example,any of the first characteristics not segmented. A third characteristicdetermining module 356 may be used to determine whether the customerdata is to be segmented according to a third characteristic. If adetermination is made that the customer data is not to be segmentedaccording to a third characteristic, a two-dimensional matrix forcross-segmenting a plurality of customers by the first and secondcharacteristics may be generated using two-dimensional matrix generatingmodule 358. The segmentation may be expanded to many data typesgenerating a multi-dimensional hypercube that more completelycharacterizes the customers.

If third characteristic determining module 356 determines that thecustomer data is to be segmented according to a third characteristic, athird characteristic segmenting module 360 may be used to segment thecustomer data according to the third characteristic. The thirdcharacteristic may be any of the characteristics not segmented by thefirst characteristic segmenting module 352 and the second characteristicsegmenting module 354. After segmenting the customer data according tothe third characteristic, a three-dimensional matrix generating module362 may be used to generate a three-dimensional matrix forcross-segmenting the plurality of customers by the first, second, andthird characteristics.

FIG. 9 illustrates a method for adaptive marketing using insight drivencustomer interaction according to one embodiment of the invention. Adatabase may be created, step 402. The database may be used to storecustomer data. The data may be cleansed (such as by removing duplicaterecords), step 404, and individualized, step 406. Individualizing thedata may include providing an identifier to customer data that indicatesthe particular customer for whom that data was gathered. This enablesthe data to be cross-referenced easily according to a customeridentifier. The customer data may be retrieved from a plurality ofdatabases, step 408. The customer data may then be loaded into, forexample, a data warehouse, step 410. The customer data may be appendedwith demographic data for each customer gathered, step 412. Thedemographic data may be gathered from external sources. When a businesswants to optimize the value of its customer relationships, it must learnto tailor customer interactions to the needs of its customers. To dothis, a company needs to obtain an integrated view of the customer,segment its customers into groups, and assess how customers behave andwill respond to various offers. Selecting data using the CAR may be thefirst step in the process, step 414. The data selected may be used tocreate a sample set of customer records for segmentation as well asdrive predictive modeling.

A sample set of customer records may be created to reduce a number ofcustomers for which customer segmenting may be performed, step 416.Preferably, the sample set is chosen at random, however, other methodsmay also be used. By using a random sample set, fewer customer recordsare used for segmentation while generating a substantially accuratedepiction of customers. The customer records may be divided intosegments, step 418. The customers within the segments may have one ormore similar characteristics. The customers within the segments may begiven a score, step 420. The score may be based on the segmentationresults and quantitatively represent a customer. The marketing offer(s)to be delivered to the customers may be determined, a delivery channeldetermined, and the segments to be targeted identified, step 422. Acampaign may be defined comprising the offers proposed to targetedsegments may be defined, step 424. The campaign may include marketingoffers such as coupons or other incentives for purchasing a particularproduct or service.

A predictive model may be built to predict how customers may react tothe marketing offers and which customers in a particular segment shouldreceive the offers, step 426. The predictive model may providepropensity scores for the customers. The propensity scores may indicatewhich customers are more likely to accept a marketing offer. Thepropensity scores may then be applied to the customer data, step 428. Acustomer list may be created to identify which customers should be giventhe offer, step 430. The campaign may then be executed, step 432. Thismay include communicating the marketing offers to the customers via acustomer interaction. The customer interaction may be a telephone callwith a telemarketer, an electronic mail message, an offer received viaregular mail, etc. Depending on how a customer reacts to the marketingoffer, the marketing campaign may be adjusted accordingly. For example,if the customer provides a telemarketer with information that thetelemarketer believes will induce the customer to accept a marketingoffer, the telemarketer may customize the marketing offer for thatcustomer.

The campaign results may be tracked, step 434. Tracking the campaign mayinclude determining which customers have redeemed a marketing offer,which marketing offer was redeemed, and the time elapsed betweencommunicating the marketing offer to the customer and when the offer wasredeemed. Campaign tracking may also include updating the database withthe campaign results. The campaign results may then be used to furtherrefine existing campaigns or to define additional campaigns. Afterrefining existing campaigns and defining one or more additionalcampaigns, the campaigns may be iterated, step 436. By iterating throughcampaigns, insight may be developed regarding how a customer may respondto marketing offers. This insight may then be used to generate morepredictive models regarding a customer's behavior toward marketingoffers in general or to specific types of marketing offers. Subsequentcampaigns may then launched, step 438, using the insight developed suchthat the subsequent campaigns may produce a higher result of redeemedoffers. The method for adaptive marketing using insight driven customerinteraction may be repeated as desired to generate additional refinedmarketing campaigns.

FIG. 10 is a block diagram of a system 500 for adaptive marketing usinginsight driven customer interaction. The system 500 may include adatabase creating module 502. The database creating module 502 may beused to create a database for storing customer data that may be used fordefining a marketing campaign. A data cleansing module 504 may be usedto cleanse the data, such as by performing de-duplication. A dataindividualizing module 506 may be used to individualize the data foreach customer that the data refers. The customer data may be retrievedfrom a plurality of databases using customer data retrieving module 508.A data loading module 510 may be used to load the cleansed andindividualized data into, for example, a data warehouse. The data maythen be appended with demographic data obtained related to each customerusing external demographic data appending module 512. The demographicdata may be, for example, obtained from an external data source.

A customer data selecting module 514 may be used to select customer datausing the CAR. The CAR preferably includes all of the information neededabout a customer that may be needed to group customers into segments andprepare predictive models. A sample set of customer records may becreated to reduce a number of customers for which customer segmentingmay be performed using sample set creating module 516. Preferably, thesample set is chosen at random, however, any method may be used. Thecustomer records may be divided into segments using customer segmentingmodule 518. The customers within the segments may have one or moresimilar characteristics. The customers within the segments may be givena score using customer segment scoring module 520. The score may bebased on the segmentation results and be used to quantitativelyrepresent the customer. The marketing offer(s) that are to be deliveredto customers may be determined along with a marketing channel and targetsegment(s) using offer, channel, and target segment determining module522. The marketing channel may be, for example, electronic mail, regularmail, facsimile, telephone call, etc. Based on the offer(s), channel(s),and segment(s) determined, a marketing campaign may be defined usingmarketing campaign defining module 524.

A predictive model may be built using predictive model building module526. The CAR prepared using CAR preparing module 254 may be used toselect the customer data needed as input to the predictive model. Thepredictive model may be used to predict customer behavior regarding howa customer may respond to particular marketing offers. The predictivemodel may generate a propensity score for the customers. The propensityscore may indicate how likely a customer is to accept a marketing offer.A higher score may indicate that customers within that segment are morelikely to accept a particular marketing offer. The propensity score maythen be applied to the customer data, step 528.

A customer list may be created to identify which customers shouldreceive the offer using customer list creating module 530. The campaignmay then be executed using campaign executing module 532. The campaignmay be executed, for example, by mailing the offers via regular mail orelectronic mail, telephoning the customers, or initiating some otherkind of customer interaction.

The results of the campaign may be tracked using campaign trackingmodule 534. The results may include, for example, which marketing offerswere accepted, which customers accepted the offers, and the time elapsedbetween offer and acceptance. This information may be used to iterateadditional campaigns using campaign iterating module 536. The resultsmay provide insight regarding customer behavior that may be used torefine additional marketing campaigns to increase the likelihood that acustomer will accept the marketing offer. The iterated campaigns may belaunched using iterated campaign launching module 538.

While the specification describes particular embodiments of the presentinvention, those of ordinary skill can devise variations of the presentinvention without departing from the inventive concept. For example,although the invention has been described in terms of a marketingcampaign, the invention may be used with any type of customerinteraction. For instance, customers who are the target of a marketingcampaign may be given an offer if the customers call a particularbusiness with a service request. Similarly, targeted customers may begiven an offer when meeting in person with a sales representative of abusiness to conduct a sales transaction.

The marketing promotion may be offered as follows. Assume that a bankhas executed the adaptive marketing steps described above to the pointof defining a marketing campaign whereby on-line banking customers witha combined family income exceeding $100,000 per year will be offered aPlatinum Mastercard™. The marketing offer may be defined in an offerdatabase when the campaign is to be executed. The customers who are thetarget of the offer may be flagged. This information may be accessed andused when a targeted customer engages with the bank for any sort oftransaction.

If the customer calls one of the bank's call centers to make a servicerequest (e.g., validate the balance in an account, make an inquiry abouta bank statement, etc), the call center agent may be given informationthat this customer is the target for the Platinum Mastercard™ promotionwhich could be offered after the service request is fulfilled.Similarly, if the customer is servicing an account using an on-linebanking application, a web-based application may determine that thecustomer is the target of the marketing promotion and deliver the offerto the customer. If the customer visits a bank branch to open an accountor buy a Certificate of Deposit, a sales agent may determine that thecustomer is the target of the promotion and offer the promotion to thecustomer.

Therefore, the adaptive marketing flow could affect all forms ofcustomer interaction across multiple customer interaction channels. Notethat the result of any interaction may be loaded into the customer datawarehouse and later extracted to retrain the analytical models andeither define new, improved marketing campaigns or to better targetexisting campaigns. All forms of interaction may benefit from andcontribute to the iterative nature of the adaptive marketing process.

While the specification describes particular embodiments of the presentinvention, those of ordinary skill can devise variations of the presentinvention without departing from the inventive concept.

1.-68. (canceled)
 69. A method for performing marketing activities,comprising: providing a system comprising a memory to storeinstructions, a processor having a plurality of software modules, and adatabase embodied on computer-readable medium to store customer data fora plurality of customers, wherein the software modules access theinstructions stored in the memory and the customer data stored in thedatabase, and wherein the plurality of software modules comprise acustomer data extracting software module and a marketing campaigndefining module; and executing the instructions by the processor toperform: extracting the customer data from the database by the customerdata extracting software module; defining a targeted marketing campaignby the marketing campaign defining module, the targeted marketingcampaign comprising a targeted marketing offer, a targeted interactionchannel, and a targeted set of customers most likely to accept thetargeted marketing offer via the targeted interaction channel, whereinthe defining further comprises: segmenting a sample set of the customersinto a plurality of customer segments; describing each of the pluralityof customer segments with lift values indicating a likelihood ofpositive response by a selected customer segment to a selected marketingoffer; selecting, based on the lift values, a prospect set from thecustomer segments for potential inclusion in a selected marketingcampaign; assessing with a predictive model a likelihood of acceptanceof each customer in the selected prospect set of the selected marketingoffer; scoring each customer in the selected prospect set with acustomer score indicating the likelihood of acceptance; selecting thetargeted set of customers based on the scoring; and executing thetargeted marketing campaign by offering the targeted marketing offer tothe targeted set of the customers via the targeted interaction channel.70. The method of claim 69, further comprising, in a closed-loopprocess, obtaining insight from campaign results data from the targetedmarketing campaign, wherein the insight is used to refine and simplifysubsequent customer data extraction and customer selection.
 71. Themethod of claim 69, further comprising: gathering campaign results datafrom the targeted marketing campaign by a campaign tracking module, thecampaign results data including a count of customers accepting thetargeted marketing offer via the targeted interaction channel, and alength of time between making of the targeted marketing offer and theaccepting of the targeted marketing offer; training the predictive modelwith the campaign results data; obtaining insight from the campaignresults data with an insight module by comparing the results of thepredictive model to the campaign results data; and developing anadditional targeted marketing campaign with the insight to deliver anadditional targeted marketing offer to an additional targeted set ofcustomers via an additional targeted interaction channel.
 72. The methodof claim 69, further comprising creating the predictive model.
 73. Themethod of claim 69, further comprising training the predictive modelingwith a training software module, and wherein the training is based onhow the customers respond to various types of the marketing offers andto various types of the interaction channels used in the targetedmarketing campaign.
 74. The method of claim 69, further comprisingdetermining a segment score for a sampled customer in the sample set andassociating the sampled customer with one of the customer segments basedon the segment score.
 75. The method of claim 69, wherein segmenting thesample set comprises overlaying value segments and behavior segments.76. A system, comprising: a processor; and a memory storing instructionsoperable with the processor for performing marketing activities, theinstructions structured throughout a plurality of modules, said modulescomprising: a customer data extracting module configured to extractcustomer data from a database; a marketing campaign defining moduleconfigured to define a targeted marketing campaign comprising a targetedmarketing offer, a targeted interaction channel, and a targeted set ofcustomers most likely to respond to the targeted marketing offer via thetargeted interaction channel; a segmenting module configured to segmenta sample set of the customers into a plurality of customer segments; asegment description module configured to describe each of the pluralityof customer segments with lift values indicating a likelihood ofpositive response by a selected customer segment to a selected marketingoffer; a prospect set selecting module configured to select, based onthe lift values, a prospect set from the plurality of customer segmentsfor potential inclusion in a selected marketing campaign; a predictivemodel module configured to assess a likelihood of acceptance of eachcustomer in the selected prospect set of the selected marketing offer;and a prospect set scoring module configured to score each customer inthe selected prospect set with a customer score indicating thelikelihood of acceptance; and a customer selection module configured toselect the targeted set of customers based on the customer score; and anexecution module configured to execute the targeted marketing campaignby offering the targeted marketing offer to the targeted set of thecustomers via the targeted interaction channel.
 77. The system of claim76, further comprising an analysis module configured to analyze insightobtained from campaign results data from the targeted marketing campaignto refine and simplify subsequent customer data extraction and customerselection,
 78. The system of claim 76, further comprising: a campaigntracking module configured to gather campaign results data from thetargeted marketing campaign, the campaign results data including a countof customers accepting the targeted marketing offer via the targetedinteraction channel, and a length of time between making of the targetedmarketing offer and the accepting of the targeted marketing offer; atraining module for training the predictive model with the campaignresults data; an insight module configured to obtain insight from thecampaign results data by comparing the results of the predictive mode tothe campaign results data; and an additional targeted marketing campaigndevelopment module configured to develop an additional targetedmarketing campaign to deliver an additional targeted marketing offer toan additional targeted set of customers via an additional targetedinteraction channel.
 79. The system of claim 76, further comprising apredictive model creating module configured to create the predictivemodel.
 80. The system of claim 76, wherein the predictive model isconfigured to be re-trained based on how the customers respond tovarious types of the marketing offers and to various types of theinteraction channels used in each targeted marketing campaign.
 81. Thesystem of claim 76, wherein the segmenting module is configured to usevariables that indicate a customer's behavior.
 82. The system of claim76, wherein the segmenting module is configured to use a customerlifetime value as a driving variable.
 83. The system of claim 76,further comprising an overlaying module configured to overlay valuesegments and behavior segments.
 84. A computer program embodied on acomputer readable medium for performing marketing activities, whereinthe computer program instructs a processor to perform a methodcomprising: accessing instructions for a plurality of software modulesstored in a memory, wherein the plurality of software modules comprise acustomer data extracting software module and a marketing campaigndefining module; accessing customer data stored in a database for aplurality of customers; and executing, by the processor, theinstructions to perform: extracting the customer data from the databaseby the customer data extracting software module; defining a targetedmarketing campaign by the marketing campaign defining module, thetargeted marketing campaign comprising a targeted marketing offer, atargeted interaction channel, and a targeted set of customers mostlikely to accept the targeted marketing offer via the targetedinteraction channel, wherein the defining further comprises: segmentinga sample set of the customers into a plurality of customer segments;describing each of the plurality of customer segments with lift valuesindicating a likelihood of positive response by a selected customersegment to a selected marketing offer; selecting, based on the liftvalues, a prospect set from the customer segments for potentialinclusion in a selected marketing campaign; assessing with a predictivemodel a likelihood of acceptance of each customer in the selectedprospect set of the selected marketing offer; scoring each customer inthe selected prospect set with a customer score indicating thelikelihood of acceptance; and selecting the targeted set of customersbased on the scoring; and executing the targeted marketing campaign byoffering the targeted marketing offer to the targeted set of thecustomers via the targeted interaction channel.
 85. The computer programof claim 84, wherein the method further comprises executing theinstructions to obtain, in a closed-loop process, insight from campaignresults data from the targeted marketing campaign, wherein the insightis used to refine and simplify subsequent customer data extraction andcustomer selection.
 86. The computer program of claim 84, wherein themethod further comprises executing the instructions to perform:gathering campaign results data from the targeted marketing campaign bya campaign tracking module, the campaign results data including a countof customers accepting the targeted marketing offer via the targetedinteraction channel, and a length of time between making of the targetedmarketing offer and the accepting of the targeted marketing offer;training the predictive model with the campaign results data; obtaininginsight from the campaign results data with an insight module bycomparing the results of the predictive model to the campaign resultsdata; and developing an additional targeted marketing campaign with theinsight to deliver an additional targeted marketing offer to anadditional targeted set of customers via an additional targetedinteraction channel.
 87. The computer program of claim 84, whereinextracting the customer data comprises selecting data from a databaseview that comprises at least a portion of data from the database and atransform that has been developed from the data from the database. 88.The computer program of claim 87, wherein a database object includes thedatabase view.